# Work out the minimal span to show all errors
gold_spans = set([(node.label, node.span[0], node.span[1])
for node in gold_tree.get_nodes()])
test_spans = set([(node.label, node.span[0], node.span[1])
for node in test_tree.get_nodes()])
diff = gold_spans.symmetric_difference(test_spans)
width = [1e5, -1]
for span in diff:
if span[2] - span[1] == 1:
continue
if span[1] < width[0]:
width[0] = span[1]
if span[2] > width[1]:
width[1] = span[2]
mprint('\n\\scalebox{\\derivscale}{', out, 'tex')
mprint(render_tree.tex_synttree(test_tree, gold_spans, span=width),
out, 'tex')
mprint(
'}\n\\small\n(a) Parser output\n\n\\vspace{3mm}\n\\scalebox{\\derivscale}{',
out, 'tex')
mprint(render_tree.tex_synttree(gold_tree, test_spans, span=width),
out, 'tex')
mprint('}\n\\small\n(b) Gold tree\n\\pagebreak', out, 'tex')
mprint("", out, 'all')
match = stats['out'][0]
gold = stats['out'][1]
test = stats['out'][2]
p, r, f = nlp_eval.calc_prf(match, gold, test)
mprint("Overall %s: %.2f %.2f %.2f" % ('out', p * 100, r * 100, f * 100),
out, 'out')
if gold_tree is not None:
treebanks.remove_trivial_unaries(gold_tree)
# Print tree
if out_format == 's':
print render_tree.text_tree(tree, single_line=True)
elif out_format == 'm':
print render_tree.text_tree(tree, single_line=False)
elif out_format == 'o':
print render_tree.text_ontonotes(tree)
elif out_format == 'p':
print render_tree.text_POS_tagged(tree)
elif out_format == 't':
if gold_tree is None:
print '\\scalebox{\\derivscale}{'
print render_tree.tex_synttree(tree)
print '}\n\\small\n\\pagebreak'
else:
print '\\scalebox{\\derivscale}{'
other_spans = gold_tree.span_dict()
print render_tree.tex_synttree(tree, other_spans)
print '}\n\\small\n\\pagebreak'
print '\\scalebox{\\derivscale}{'
other_spans = tree.span_dict()
print render_tree.tex_synttree(gold_tree, other_spans)
print '}\n\\small\n\\pagebreak'
elif out_format == 'w':
print render_tree.text_words(tree)
if out_format == 't':
print '\\end{document}'
if gold_tree is not None:
treebanks.remove_trivial_unaries(gold_tree)
# Print tree
if out_format == 's':
print render_tree.text_tree(tree, single_line=True)
elif out_format == 'm':
print render_tree.text_tree(tree, single_line=False)
elif out_format == 'o':
print render_tree.text_ontonotes(tree)
elif out_format == 'p':
print render_tree.text_POS_tagged(tree)
elif out_format == 't':
if gold_tree is None:
print '\\scalebox{\\derivscale}{'
print render_tree.tex_synttree(tree)
print '}\n\\small\n\\pagebreak'
else:
print '\\scalebox{\\derivscale}{'
other_spans = gold_tree.span_dict()
print render_tree.tex_synttree(tree, other_spans)
print '}\n\\small\n\\pagebreak'
print '\\scalebox{\\derivscale}{'
other_spans = tree.span_dict()
print render_tree.tex_synttree(gold_tree, other_spans)
print '}\n\\small\n\\pagebreak'
elif out_format == 'w':
print render_tree.text_words(tree)
if out_format == 't':
print '\\end{document}'
stats['out'][2] += test
p, r, f = nlp_eval.calc_prf(match, gold, test)
mprint("Eval: %.2f %.2f %.2f" % (p*100, r*100, f*100), out, 'out')
# Work out the minimal span to show all errors
gold_spans = set([(node.label, node.span[0], node.span[1]) for node in gold_tree.get_nodes()])
test_spans = set([(node.label, node.span[0], node.span[1]) for node in test_tree.get_nodes()])
diff = gold_spans.symmetric_difference(test_spans)
width = [1e5, -1]
for span in diff:
if span[2] - span[1] == 1:
continue
if span[1] < width[0]:
width[0] = span[1]
if span[2] > width[1]:
width[1] = span[2]
mprint('\n\\scalebox{\\derivscale}{', out, 'tex')
mprint(render_tree.tex_synttree(test_tree, gold_spans, span=width), out, 'tex')
mprint( '}\n\\small\n(a) Parser output\n\n\\vspace{3mm}\n\\scalebox{\\derivscale}{', out, 'tex')
mprint(render_tree.tex_synttree(gold_tree, test_spans, span=width), out, 'tex')
mprint( '}\n\\small\n(b) Gold tree\n\\pagebreak', out, 'tex')
mprint("", out, 'all')
match = stats['out'][0]
gold = stats['out'][1]
test = stats['out'][2]
p, r, f = nlp_eval.calc_prf(match, gold, test)
mprint("Overall %s: %.2f %.2f %.2f" % ('out', p*100, r*100, f*100), out, 'out')
mprint('\\end{document}', out, 'tex')
